In this recipe, we will explore some interesting plots that are for presenting and analyzing the results from mixed effects models. In the simplest formulation of mixed effects models, we have a random intercept by group. Every observation belonging to the same group will share that very same shock, rendering all of them correlated. But this can be extended to other coefficients (not just the intercept). We could have yet another coefficient, that is, beta would be the sum of beta1 (which would be fixed) and beta_random (this would be a random effect). What this would imply is that the slope relating to the regressor and the response, would have two parts: a part that is the same for all the observations, and another part that depends on each group.